Multi-environment genomic prediction for soluble solids content in peach (Prunus persica) - Hardner, Craig M.; Fikere, Mulusew; Gasic, Ksenija; da Silva Linge, Cassia; Worthington, Margaret; Byrne, David; Rawandoozi, Zena; Peace, Cameron (Frontiers in Plant Science, 2022)
Genotype-by-environment interaction (G×E) is a common phenomenon influencing genetic improvement in plants and a good understanding of this phenomenon is important for breeding and cultivar deployment strategies. However, there is little information on G×E in horticultural tree crops, mostly due to evaluation costs, leading to a focus on development and deployment of locally adapted germplasm. Using sweetness (measured as soluble solids content, SSC) in peach/nectarine assessed at four trials from three U.S. peach breeding programs as a case study, we evaluated the hypotheses that (i) complex data from multiple breeding programs can be connected using GBLUP models to improve knowledge of G×E for breeding and deployment and (ii) accounting for a known large-effect quantitative trait locus (QTL) improves prediction accuracy. Following a structured strategy using univariate and multivariate models containing additive and dominance genomic effects on SSC, a model that included a previously detected QTL and background genomic effects was a significantly better fit than a genome-wide model with completely anonymous markers. Estimates of individual narrow-sense and broad-sense heritability for SSC were high (0.57–0.73, 0.66–0.80, respectively), with 19–32% of total genomic variance explained by the QTL. Genome-wide dominance effects and QTL effects were stable across environments. Significant G×E was detected for background genome effects, mostly due to low correlation of these effects across seasons within a particular trial. Expected prediction accuracy, estimated from the linear model, was higher than realised prediction accuracy estimated by cross-validation, suggesting these two parameters measure different qualities of the prediction models. While prediction accuracy was improved in some cases by combining data across trials, particularly when phenotypic data for untested individuals was available from other trials, this improvement was not consistent. This study confirms that complex data can be combined into a single analysis using GBLUP methods to improve understanding of G×E and incorporate known QTL effects. In addition, the study generated baseline information to account for population structure in genomic prediction models in horticultural crop improvement.